MOP-ND 🚴

GR
Gianluca Rea

🚴 Overview

MOP-ND is a university data analytics and optimization project where I implemented a Multi-commodity Orienteering Problem with Network Design (MOP_ND) model for the "Data Analytics and Data Driven Decision" course at the University of L'Aquila.

The project studies how to design high-quality origin-destination itineraries for different classes of cycle-tourists, inspired by the paper "Designing single origin-destination itineraries for several classes of cycle-tourists".

🦾 Repository

Code and notebooks: github.com/gianlucarea/mop-nd

Why this project matters

Route planning in cycling tourism is a multi-objective problem: distance, attractiveness, and user preferences often pull in different directions. The goal of this project was to model that tradeoff and compute practical itineraries for multiple user groups in a shared network.

In practice, the work focuses on:

  • →Designing routes for multiple tourist classes
  • →Optimizing network design decisions for cycling scenarios
  • →Balancing route quality, travel constraints, and preferences
  • →Maximizing overall satisfaction across itineraries

🛠️ Tech Stack

  • →Python for model implementation and experimentation
  • →Jupyter Notebooks for interactive analysis
  • →Gurobi as the mathematical optimization solver
  • →NumPy for numerical operations
  • →NetworkX for graph and network modeling
  • →Matplotlib for visualizing outputs and comparisons
  • →Itertools for combinatorial utilities

Modeling workflow

The project is organized as an experimentation workflow in notebooks:

  1. →Define the network and candidate routes.
  2. →Encode constraints and objective terms for each tourist class.
  3. →Run optimization with Gurobi.
  4. →Compare candidate solutions and interpret tradeoffs.
  5. →Visualize resulting itineraries and key metrics.

🧪 Getting Started

Requirements:

  • →Gurobi (optimization solver)
  • →NumPy
  • →NetworkX
  • →Matplotlib
  • →Itertools
  • →Jupyter

The repository is notebook-driven and aimed at reproducible analysis. After installing dependencies (and a valid Gurobi setup), open the notebooks and execute cells in order to reproduce model runs and charts.

📚 Learn More

For additional context on the optimization formulation, refer to the linked paper and the implementation notebooks in the repository.

What I learned

  • →How to translate research-style formulations into executable optimization models
  • →How to reason about tradeoffs between competing objectives in network design
  • →How to structure notebook experiments so results are easier to compare and explain

🤝 Contributing

Contributions are welcome. If you want to improve the formulation, datasets, or notebook clarity, feel free to open a pull request.

  1. →Fork the repository
  2. →Create a feature branch: git checkout -b feature-name
  3. →Commit your changes: git commit -m 'Add new feature'
  4. →Push to the branch: git push origin feature-name
  5. →Open a Pull Request

Please ensure your code follows the existing style and includes appropriate documentation.

📧 Contact

For questions, feedback, or collaboration opportunities, feel free to reach out:

This project reflects my interest in optimization, applied data science, and decision-support systems for real-world mobility problems.